Your research.
Straight into Python
with our SDK.

Check earnings quality. Map ownership to liquidity. Put options, positioning and macro data in context.

Install from PyPI Explore the notebooks

One install. Your first request.

Install the public package, set your QuantJourney API key and create a client.

  1. InstallUse a virtual environment with Python 3.11 or later. These examples use the public PyPI release, quantjourney 2.4.0.
  2. ConnectThe client reads QJ_API_KEY from your environment.
  3. RequestYour plan, provider permissions and configured credentials determine access.
Get an API key
TerminalInstall and configure
python -m pip install quantjourney pandas numpy matplotlib
export QJ_API_KEY="<your QuantJourney API key>"
PythonRead the key from your environment
from quantjourney.sdk import QuantJourneyAPI

qj = QuantJourneyAPI.from_env()

response = qj.eod.get_historical_prices(
    symbol="AAPL",
    start_date="2024-01-01",
    end_date="2024-12-31",
    frequency="1d",
)

Ask for the data. Use a domain route.

Domain routes name the operation through /d/. Keep a provider fixed when your calculations use its specific fields.

The workflows below show both domain calls and provider methods. Each uses the same SDK client.

Domain routingSDK → /d/ → provider
from quantjourney.sdk import QuantJourneyAPI

qj = QuantJourneyAPI.from_env()

# Inspect the available providers and route contract.
route = "macro.us.get_cpi"
contract = qj.domains.describe(route)

cpi = qj.domains.call(
    route, provider="bls",
    category="all", seasonally_adjusted=True,
    start_year=2020, end_year=2025,
)

# The HTTP operation is:
# POST /d/macro.us.get_cpi
# The Gateway resolves the provider credentials.

Twenty-nine workflows. At least two data providers in every example. Combine portfolio risk, options, regulatory reports, macro releases, identity and prediction markets. Start with the Gateway calls and result. Download the full code for date alignment, calculations and plotting.

Saved calculations use observed API responses captured on 31 October 2026 UTC. Re-run with your credentials; availability and results can change.

Showing 29 workflows

01

How does FX change your portfolio’s tail risk?

Translate a multi-asset allocation into EUR, estimate historical VaR and Expected Shortfall, and inspect high-VIX observations.

Domain routes · /d/ + provider methodsEOD + ECB + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_portfolio_var import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'prices': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['SPY', 'TLT', 'GLD', 'DBC', 'UUP'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
    'ecb_fx': qj.domains.call('macro.eu.get_ecb_series',
        provider='ecb', series_id='EXR.D.USD.EUR.SP00.A', start_date='2020-01-01', end_date='2025-12-31'),
    'vix_history': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data01 / 29
How does FX change your portfolio’s tail risk? Notebook allocation: SPY 42%, TLT 22%, GLD 14%, DBC 12%, UUP 10%, daily rebalanced before costs. EUR translation uses ECB reference rates. 95% historical one-day loss estimates; high VIX uses the previous session and a sample 85th-percentile cutoff. Not a forecast.
Notebook allocation: SPY 42%, TLT 22%, GLD 14%, DBC 12%, UUP 10%, daily rebalanced before costs. EUR translation uses ECB reference rates. 95% historical one-day loss estimates; high VIX uses the previous session and a sample 85th-percentile cutoff. Not a forecast.Saved calculation · API inputs captured 31 October 2026
02

What risks sit behind the stock returns?

Align monthly adjusted returns and the five Fama–French factors, subtract the risk-free return, then estimate exposures.

Provider methodsEOD + Fama–French
from quantjourney.sdk import QuantJourneyAPI
from sdk_factor_exposure import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'prices': qj.eod.get_historical_prices(
        symbols=['AAPL', 'MSFT', 'SPY'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
    'factors': qj.ff.get_factors(
        region='america', factor='5_factors'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data02 / 29
What risks sit behind the stock returns? AAPL, MSFT and SPY, monthly common observations in 2020–2025. Five-factor OLS with an intercept. Exposures describe the sample; they are not forecasts or portfolio recommendations.
AAPL, MSFT and SPY, monthly common observations in 2020–2025. Five-factor OLS with an intercept. Exposures describe the sample; they are not forecasts or portfolio recommendations.Saved calculation · API inputs captured 31 October 2026
03

Where is the option market pricing the skew?

Find a live expiration, filter actual option observations and compare call and put implied volatility against a daily-price reference.

Provider methodsCboe + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_smile import analyze, AS_OF, payload, timedelta

qj = QuantJourneyAPI.from_env()
available = payload(qj.cboe.get_options_expirations(symbol='SPY'))
if isinstance(available, dict):
    available = available['expirations']
dates = sorted((str(item)[:10] for item in available if str(item)[:10] >= AS_OF.isoformat()))
if not dates:
    raise ValueError('No nonexpired SPY option expiration returned')
raw = {
    'chain': qj.cboe.get_options_chain(
        symbol='SPY', expiration_date=dates[0]),
    'spy_spot': qj.eod.get_historical_prices(
        symbols=['SPY'], start_date=(AS_OF - timedelta(days=14)).isoformat(), end_date=(AS_OF - timedelta(days=1)).isoformat(), frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data03 / 29
Where is the option market pricing the skew? Observed SPY chain, expiry 2026-11-02; EOD reference close dated 2026-10-30. Zero IV and invalid bid/ask observations are excluded. The daily close is not a synchronized option quote.
Observed SPY chain, expiry 2026-11-02; EOD reference close dated 2026-10-30. Zero IV and invalid bid/ask observations are excluded. The daily close is not a synchronized option quote.Saved calculation · API inputs captured 31 October 2026
04

What is the volatility curve saying?

Compare delayed VIX futures, the latest published VIX close and realised equity volatility with explicit observation dates.

Domain routes · /d/ + provider methodsCboe + FRED + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_vix_curve import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'vix_curve': qj.domains.call('derivatives.vol.get_vix_term_structure',
        provider='cboe'),
    'vix_current': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='VIXCLS', start='2026-01-01', end='2026-10-30'),
    'recent_prices': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['SPY'], start_date='2026-01-01', end_date='2026-10-30', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data04 / 29
What is the volatility curve saying? Delayed Cboe futures snapshot captured 2026-10-31. Reference lines show the published VIX close dated 2026-10-29 and 21-session SPY realised volatility through 2026-10-30. Maturities and timestamps differ; these are not interchangeable prices.
Delayed Cboe futures snapshot captured 2026-10-31. Reference lines show the published VIX close dated 2026-10-29 and 21-session SPY realised volatility through 2026-10-30. Maturities and timestamps differ; these are not interchangeable prices.Saved calculation · API inputs captured 31 October 2026
05

What does a put spread protect at expiry?

Select strikes from an observed option chain, use executable sides of the quote and calculate the protected payoff per underlying share.

Domain routes · /d/ + provider methodsCboe + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_option_overlay import analyze, AS_OF, payload, timedelta

qj = QuantJourneyAPI.from_env()
dates = payload(qj.domains.call('derivatives.options.get_options_expirations', provider='cboe', symbol='SPY'))
dates = dates['expirations'] if isinstance(dates, dict) else dates
available = sorted((str(day)[:10] for day in dates if str(day)[:10] >= AS_OF.isoformat()))
if not available:
    raise ValueError('No nonexpired SPY option expiration')
raw = {
    'chain': qj.domains.call('derivatives.options.get_options_chain',
        provider='cboe', symbol='SPY', expiration_date=available[0]),
    'spy_spot': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['SPY'], start_date=(AS_OF - timedelta(days=14)).isoformat(), end_date=(AS_OF - timedelta(days=1)).isoformat(), frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data05 / 29
What does a put spread protect at expiry? SPY observed chain, expiry 2026-11-02; reference daily close 2026-10-30. Strike targets are 97% and 90% of the reference. Entry uses long ask minus short bid. The chart is an expiry scenario calculation, before fees, financing and assignment effects.
SPY observed chain, expiry 2026-11-02; reference daily close 2026-10-30. Strike targets are 97% and 90% of the reference. Entry uses long ask minus short bid. The chart is an expiry scenario calculation, before fees, financing and assignment effects.Saved calculation · API inputs captured 31 October 2026
06

How large are holders against trading volume?

Compare reported positions with normal and high-volatility trading volume observed before the holdings date.

Domain routes · /d/ + provider methodsFMP + EOD + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_ownership_capacity import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'vix_history': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
    'holders': qj.domains.call('regulatory.ownership.get_institutional_holders',
        provider='fmp', symbol='AAPL', year=2025, quarter=3, limit=100),
    'prices': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['AAPL'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data06 / 29
How large are holders against trading volume? AAPL 2025Q3. Top ten share positions within 100 returned records, labelled by SEC CIK. Volume denominators are the prior 20 sessions and the highest-VIX 15% of the prior 252 sessions. Both windows end at the holdings date. These are 100%-ADV equivalents, not execution forecasts; 13F disclosures arrive later.
AAPL 2025Q3. Top ten share positions within 100 returned records, labelled by SEC CIK. Volume denominators are the prior 20 sessions and the highest-VIX 15% of the prior 252 sessions. Both windows end at the holdings date. These are 100%-ADV equivalents, not execution forecasts; 13F disclosures arrive later.Saved calculation · API inputs captured 31 October 2026
07

Is speculative positioning stretched?

Normalize net noncommercial futures-and-options positions by open interest and compare them with the equity-market path.

Provider methodsCFTC + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_cot import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'cot': qj.cftc.get_cot_data(
        symbol='13874A', report_type='legacy', start_date='2020-01-01', end_date='2025-12-31'),
    'prices': qj.eod.get_historical_prices(
        symbols=['SPY'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data07 / 29
Is speculative positioning stretched? E-mini S&P 500 legacy combined COT, contract 13874A, 2020–2025. The axis uses report dates, not publication dates. SPY is market context, not the futures contract or strategy P&L.
E-mini S&P 500 legacy combined COT, contract 13874A, 2020–2025. The axis uses report dates, not publication dates. SPY is market context, not the futures contract or strategy P&L.Saved calculation · API inputs captured 31 October 2026
08

Does the short-interest denominator matter?

Compare FINRA’s reported days to cover with a clearly defined volume denominator aligned to the settlement date.

Provider methodsFINRA + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_short_liquidity import analyze, frame, pd

qj = QuantJourneyAPI.from_env()
short = qj.finra.get_short_interest(symbol='AAPL')
settlement = frame(short, 'settlement_date').iloc[-1]['settlement_date']
raw = {
    'short': short,
    'short_prices': qj.eod.get_historical_prices(
        symbols=['AAPL'], start_date=(settlement - pd.Timedelta(days=120)).date().isoformat(), end_date=settlement.date().isoformat(), frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data08 / 29
Does the short-interest denominator matter? AAPL short-interest settlement: 2026-10-15. FINRA’s published denominator and an EOD trailing 20-session denominator can differ. Missing coverage raises an error.
AAPL short-interest settlement: 2026-10-15. FINRA’s published denominator and an EOD trailing 20-session denominator can differ. Missing coverage raises an error.Saved calculation · API inputs captured 31 October 2026
09

Did earnings surprises move the stock?

Join reported EPS surprises to adjusted-price windows and inspect the size and direction of the subsequent move.

Provider methodsFMP + EOD + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_earnings_reaction import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'vix_history': qj.fred.get_fred_data_series_by_id(
        search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
    'earnings': qj.fmp.get_earnings_surprises(
        symbol='AAPL'),
    'prices': qj.eod.get_historical_prices(
        symbols=['AAPL'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data09 / 29
Did earnings surprises move the stock? AAPL events within 2020–2025 price coverage. Entry is the first closing price after the reported event date; exit is five sessions later. Descriptive, without a causal claim. VIX before the event date supplies market-risk context; it does not explain the return.
AAPL events within 2020–2025 price coverage. Entry is the first closing price after the reported event date; exit is five sessions later. Descriptive, without a causal claim. VIX before the event date supplies market-risk context; it does not explain the return.Saved calculation · API inputs captured 31 October 2026
10

What growth do analyst estimates imply?

Compare filed and expected EPS, then check whether the latest reported short position increased.

Domain routes · /d/ + provider methodsFMP + SEC + FINRA
from quantjourney.sdk import QuantJourneyAPI
from sdk_expectations import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'short': qj.domains.call('equity.shorting.get_short_interest',
        provider='finra', symbol='AAPL'),
    'income': qj.domains.call('equity.fundamentals.get_income_statement',
        provider='fmp', symbol='AAPL', period='annual', limit=5),
    'filings': qj.domains.call('regulatory.filings.get_company_filings',
        provider='sec', symbol='AAPL', form_type='10-K', limit=12),
    'estimates': qj.fmp.get_analyst_estimates(
        symbol='AAPL', period='annual', limit=8),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data10 / 29
What growth do analyst estimates imply? AAPL EPS actuals are matched to SEC filings. Future consensus is a snapshot, not revisions. The short-interest comparison uses FINRA’s latest and prior reported positions, settlement 2026-10-15.
AAPL EPS actuals are matched to SEC filings. Future consensus is a snapshot, not revisions. The short-interest comparison uses FINRA’s latest and prior reported positions, settlement 2026-10-15.Saved calculation · API inputs captured 31 October 2026
11

Do earnings turn into cash?

Verify filing dates, measure earnings-to-cash conversion and calculate inflation-adjusted free-cash-flow growth.

Domain routes · /d/ + provider methodsFMP + SEC + US Bureau of Labor Statistics
from quantjourney.sdk import QuantJourneyAPI
from sdk_cash_conversion import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'bls_cpi': qj.domains.call('macro.us.get_cpi',
        provider='bls', start_year=2020, end_year=2025, category='all', seasonally_adjusted=True),
    'income': qj.domains.call('equity.fundamentals.get_income_statement',
        provider='fmp', symbol='AAPL', period='annual', limit=5),
    'cash': qj.domains.call('equity.fundamentals.get_cash_flow_statement',
        provider='fmp', symbol='AAPL', period='annual', limit=5),
    'filings': qj.domains.call('regulatory.filings.get_company_filings',
        provider='sec', symbol='AAPL', form_type='10-K', limit=12),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data11 / 29
Do earnings turn into cash? AAPL FY2021–2025. Cash conversion uses matching fiscal periods and SEC acceptance dates. Real free-cash-flow growth uses fiscal-year average BLS CPI; current-vintage observations.
AAPL FY2021–2025. Cash conversion uses matching fiscal periods and SEC acceptance dates. Real free-cash-flow growth uses fiscal-year average BLS CPI; current-vintage observations.Saved calculation · API inputs captured 31 October 2026
12

Are credit spreads and bond returns telling the same story?

Put high-yield option-adjusted spreads beside high-yield versus investment-grade ETF returns, using only common dates.

Provider methodsFRED + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_credit import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'credit': qj.fred.get_fred_data_series_by_id(
        search_id='BAMLH0A0HYM2', start='2020-01-01', end='2025-12-31'),
    'prices': qj.eod.get_historical_prices(
        symbols=['HYG', 'LQD'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data12 / 29
Are credit spreads and bond returns telling the same story? Common available monthly observations in 2023–2025. HY OAS is a spread level; HYG minus LQD is a relative adjusted return. These have different units and risks.
Common available monthly observations in 2023–2025. HY OAS is a spread level; HYG minus LQD is a relative adjusted return. These have different units and risks.Saved calculation · API inputs captured 31 October 2026
13

Do inflation differences line up with the currency move?

Align US inflation, German inflation and ECB exchange rates on a monthly calendar before comparing their relationship.

Domain routes · /d/ + provider methodsOECD + US Bureau of Labor Statistics + ECB
from quantjourney.sdk import QuantJourneyAPI
from sdk_macro_fx import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'oecd_cpi': qj.domains.call('macro.series.get_cpi_data',
        provider='oecd', countries=['USA', 'DEU'], start_date='2020-01-01', end_date='2025-12-31'),
    'bls_cpi': qj.domains.call('macro.us.get_cpi',
        provider='bls', start_year=2020, end_year=2025, category='all', seasonally_adjusted=True),
    'ecb_fx': qj.domains.call('macro.eu.get_ecb_series',
        provider='ecb', series_id='EXR.D.USD.EUR.SP00.A', start_date='2020-01-01', end_date='2025-12-31'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data13 / 29
Do inflation differences line up with the currency move? US CPI from BLS, German CPI from the OECD compilation, and USD per EUR from ECB. Monthly common observations in 2021–2025. Current-vintage inflation and reference FX; descriptive, not a causal or release-time trading test.
US CPI from BLS, German CPI from the OECD compilation, and USD per EUR from ECB. Monthly common observations in 2021–2025. Current-vintage inflation and reference FX; descriptive, not a causal or release-time trading test.Saved calculation · API inputs captured 31 October 2026
14

Which sectors moved with inflation?

Join actual CPI observations with sector returns relative to SPY, then compare descriptive relationships across the sample.

Provider methodsFRED + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_inflation import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'cpi': qj.fred.get_fred_data_series_by_id(
        search_id='CPIAUCSL', start='2020-01-01', end='2025-12-31', frequency='m'),
    'prices': qj.eod.get_historical_prices(
        symbols=['SPY', 'XLE', 'XLK', 'XLF', 'XLU', 'XLP', 'XLY'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data14 / 29
Which sectors moved with inflation? Monthly common observations in 2021–2025. CPI YoY level versus sector-minus-SPY monthly return correlations. Current-vintage CPI; no release-time strategy or causal inference.
Monthly common observations in 2021–2025. CPI YoY level versus sector-minus-SPY monthly return correlations. Current-vintage CPI; no release-time strategy or causal inference.Saved calculation · API inputs captured 31 October 2026
15

How did bank margins and deposits change?

Join regulatory bank financials to policy rates and inflation, then distinguish deposit growth from purchasing-power growth.

Domain routes · /d/ + provider methodsFDIC + FRED + US Bureau of Labor Statistics
from quantjourney.sdk import QuantJourneyAPI
from sdk_bank_cycle import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'fdic_bank': qj.domains.call('regulatory.banking.get_institutions',
        provider='fdic', filters='CERT:628', limit=1),
    'fdic_history': qj.domains.call('regulatory.banking.get_financials',
        provider='fdic', filters='CERT:628 AND REPDTE:[20200101 TO 20251231]', limit=30, sort_by='REPDTE', sort_order='ASC'),
    'funds': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='DFF', start='2020-01-01', end='2025-12-31'),
    'bls_cpi': qj.domains.call('macro.us.get_cpi',
        provider='bls', start_year=2020, end_year=2025, category='all', seasonally_adjusted=True),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data15 / 29
How did bank margins and deposits change? FDIC certificate 628, 2020–2025 quarterly reports. NIMY is the provider’s year-to-date annualised margin. Rates and CPI use quarter averages. Real deposit growth adjusts balance-sheet deposits for CPI; it does not measure customer flows.
FDIC certificate 628, 2020–2025 quarterly reports. NIMY is the provider’s year-to-date annualised margin. Rates and CPI use quarter averages. Real deposit growth adjusts balance-sheet deposits for CPI; it does not measure customer flows.Saved calculation · API inputs captured 31 October 2026
16

Does perp funding agree with the spot move?

Aggregate actual funding observations by UTC day and align them with Bitcoin spot returns from a separate source.

Provider methodsCCXT / Binance + CoinGecko
from quantjourney.sdk import QuantJourneyAPI
from sdk_crypto_funding import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'funding': qj.ccxt.get_historical_funding_rates(
        exchange='binance', symbol='BTC/USDT:USDT', limit=300),
    'crypto': qj.coingecko.get_historical_prices(
        coin_id='bitcoin', vs_currency='usd', days=365, interval='daily'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data16 / 29
Does perp funding agree with the spot move? BTC perpetual funding and BTC/USD spot, common completed UTC days in July–October 2026. Funding for day D is paired with the spot move from D 00:00 to D+1 00:00. Three funding records are required; no trade-profit claim.
BTC perpetual funding and BTC/USD spot, common completed UTC days in July–October 2026. Funding for day D is paired with the spot move from D 00:00 to D+1 00:00. Three funding records are required; no trade-profit claim.Saved calculation · API inputs captured 31 October 2026
17

Does sentiment agree with observed market risk?

Combine CNN Fear & Greed observations, VIX closes and SPY returns without treating sentiment as a trade signal.

Domain routes · /d/ + provider methodsCNN Fear & Greed + Cboe VIX via FRED + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_sentiment import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'cnn': qj.domains.call('sentiment.fear_greed.get_index_range',
        provider='cnnf', start_date='2026-08-01', end_date='2026-10-30'),
    'vix_current': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='VIXCLS', start='2026-01-01', end='2026-10-30'),
    'recent_prices': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['SPY'], start_date='2026-01-01', end_date='2026-10-30', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data17 / 29
Does sentiment agree with observed market risk? Common observations in August–October 2026. CNN records have dates without intraday timestamps, so the chart uses the mean of available observations per day. VIX and SPY use daily observations; this is market context, not signal performance.
Common observations in August–October 2026. CNN records have dates without intraday timestamps, so the chart uses the mean of available observations per day. VIX and SPY use daily observations; this is market context, not signal performance.Saved calculation · API inputs captured 31 October 2026
18

Is capital earning more than the benchmark?

Calculate a reported-capital return proxy and compare it with the Treasury yield observed during each fiscal year.

Provider methodsFMP + FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_capital_returns import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'income': qj.fmp.get_income_statement(
        symbol='AAPL', period='annual', limit=5),
    'balance': qj.fmp.get_balance_sheet_statement(
        symbol='AAPL', period='annual', limit=5),
    'rates_all': qj.fred.get_fred_data_series_by_id(
        search_id='DGS10', start='2020-01-01', end='2025-12-31'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data18 / 29
Is capital earning more than the benchmark? AAPL FY2022–2025. After-tax operating income / average debt plus equity less cash. The 10Y Treasury yield is a benchmark, not WACC.
AAPL FY2022–2025. After-tax operating income / average debt plus equity less cash. The 10Y Treasury yield is a benchmark, not WACC.Saved calculation · API inputs captured 31 October 2026
19

How much revenue growth survives inflation?

Deflate reported revenue with CPI averaged over the matching fiscal year, rather than comparing mismatched calendar dates.

Provider methodsFMP + US Bureau of Labor Statistics
from quantjourney.sdk import QuantJourneyAPI
from sdk_real_growth import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'income': qj.fmp.get_income_statement(
        symbol='AAPL', period='annual', limit=5),
    'bls_cpi': qj.bls.get_cpi(
        start_year=2020, end_year=2025, category='all', seasonally_adjusted=True),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data19 / 29
How much revenue growth survives inflation? AAPL FY2022–2025. CPI is a broad US purchasing-power reference, not a company-specific price deflator. Current-vintage macro observations.
AAPL FY2022–2025. CPI is a broad US purchasing-power reference, not a company-specific price deflator. Current-vintage macro observations.Saved calculation · API inputs captured 31 October 2026
20

What happened after the annual filing?

Use the SEC acceptance timestamp as the event anchor and measure a conservative post-filing return window.

Domain routes · /d/ + provider methodsSEC + EOD + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_filing_reaction import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'vix_history': qj.domains.call('macro.indicators.get_fred_data_series_by_id',
        provider='fred', search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
    'filings': qj.domains.call('regulatory.filings.get_company_filings',
        provider='sec', symbol='AAPL', form_type='10-K', limit=12),
    'prices': qj.domains.call('equity.pricing.get_historical_prices',
        provider='eod', symbols=['AAPL'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data20 / 29
What happened after the annual filing? AAPL 10-K filings within 2020–2025 price coverage. The return window starts at the next session close after the UTC acceptance date. No attribution of price moves to filings. VIX before the event date supplies market-risk context; it does not explain the return.
AAPL 10-K filings within 2020–2025 price coverage. The return window starts at the next session close after the UTC acceptance date. No attribution of price moves to filings. VIX before the event date supplies market-risk context; it does not explain the return.Saved calculation · API inputs captured 31 October 2026
21

When did the numbers become available?

Match reported fiscal periods to SEC acceptance dates before using fundamentals in research.

Provider methodsFMP + SEC
from quantjourney.sdk import QuantJourneyAPI
from sdk_reporting_dates import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'income': qj.fmp.get_income_statement(
        symbol='AAPL', period='annual', limit=5),
    'filings': qj.sec.get_company_filings(
        symbol='AAPL', form_type='10-K', limit=12),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Adapted from this notebook. The downloadable code produces this analysis.

Calculated from observed API data21 / 29
When did the numbers become available? AAPL annual reports, FY2021–2025. SEC acceptance dates establish when the filing became available; fiscal year-end is not the publication date.
AAPL annual reports, FY2021–2025. SEC acceptance dates establish when the filing became available; fiscal year-end is not the publication date.Saved calculation · API inputs captured 31 October 2026
22

Which legal entity sits behind the bank?

Match the registration authority and bank certificate, then link the bank’s LEI to its reported ultimate parent.

Domain routes · /d/GLEIF + FDIC
from quantjourney.sdk import QuantJourneyAPI
from sdk_entity_identity import analyze, linked_bank

qj = QuantJourneyAPI.from_env()
raw = {
    'fdic_bank': qj.domains.call('regulatory.banking.get_institutions',
        provider='fdic', filters='CERT:628', limit=1),
    'lei_search': qj.domains.call('reference.entities.search_lei_records',
        provider='gleif', query='JPMorgan Chase Bank', country='US', entity_status='ACTIVE', registration_status='ISSUED', page_size=10),
}
_, record = linked_bank(raw)
raw['lei_parent'] = qj.domains.call('reference.entities.get_ultimate_parent', provider='gleif', lei=record['id'])
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data22 / 29
Which legal entity sits behind the bank? FDIC certificate 628 matches GLEIF authority RA000744 and registration identifier 628. The bank and its ultimate parent have different LEIs. Current records captured 2026-10-31 UTC; this is not a historical ownership graph.
FDIC certificate 628 matches GLEIF authority RA000744 and registration identifier 628. The bank and its ultimate parent have different LEIs. Current records captured 2026-10-31 UTC; this is not a historical ownership graph.Saved calculation · API inputs captured 31 October 2026
23

What does the inflation market price beyond the last release?

Compare quoted CPI threshold contracts with the latest calculated year-on-year inflation from BLS. Preserve each bid and ask.

Domain routes · /d/Kalshi + US Bureau of Labor Statistics
from quantjourney.sdk import QuantJourneyAPI
from sdk_inflation_market import analyze, AS_OF

qj = QuantJourneyAPI.from_env()
raw = {
    'kalshi_cpi': qj.domains.call('predictions.markets.search_markets',
        provider='kalshi', series_ticker='KXCPIYOY', status='open', limit=1000),
    'bls_latest': qj.domains.call('macro.us.get_cpi',
        provider='bls', category='all', seasonally_adjusted=False, start_year=AS_OF.year - 1, end_year=AS_OF.year),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data23 / 29
What does the inflation market price beyond the last release? Nearest returned open event: November 2026 CPI, closing 2026-12-10. BLS baseline: September 2026, calculated from unadjusted indexes. The rules use the twelve-month CPI increase rounded to one decimal. Quotes show a percentage of the $1 payout, not a calibrated probability distribution.
Nearest returned open event: November 2026 CPI, closing 2026-12-10. BLS baseline: September 2026, calculated from unadjusted indexes. The rules use the twelve-month CPI increase rounded to one decimal. Quotes show a percentage of the $1 payout, not a calibrated probability distribution.Saved calculation · API inputs captured 31 October 2026
24

What drove 80% of the portfolio’s losses?

Locate the largest observed drawdown, attribute the loss to each holding and compare the period with VIX.

Gateway SDK · provider methodsEOD + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_drawdown_attribution import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'prices': qj.eod.get_historical_prices(
        symbols=['SPY', 'TLT', 'GLD', 'DBC', 'UUP'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
    'vix_history': qj.fred.get_fred_data_series_by_id(
        search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
Open the full notebook

Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data24 / 29
What drove 80% of the portfolio’s losses? The linked notebook allocation is a model parameter: SPY 42%, TLT 22%, GLD 14%, DBC 12%, UUP 10%. Buy-and-hold contributions reconcile exactly to the selected peak-to-trough drawdown. The 80% cutoff refers to gross losing contributions, before offsets from winners.
The linked notebook allocation is a model parameter: SPY 42%, TLT 22%, GLD 14%, DBC 12%, UUP 10%. Buy-and-hold contributions reconcile exactly to the selected peak-to-trough drawdown. The 80% cutoff refers to gross losing contributions, before offsets from winners.Saved calculation · API inputs captured 31 October 2026
25

Which names have rising EPS estimates but falling prices?

Compare next-quarter EPS revisions with 30-calendar-day price returns and retain the latest reported earnings date.

Gateway SDK · provider methodsEOD + FMP
from quantjourney.sdk import QuantJourneyAPI
from sdk_earnings_revisions import analyze, AS_OF, timedelta

qj = QuantJourneyAPI.from_env()
raw = {
    'trends': qj.eod.get_trends_calendar(
        symbols=['AAPL.US', 'MSFT.US', 'NVDA.US']),
    'revision_prices': qj.eod.get_historical_prices(
        symbols=['AAPL', 'MSFT', 'NVDA'], start_date=str(AS_OF - timedelta(days=90)), end_date=str(AS_OF), frequency='1d'),
}
for symbol, key in [('AAPL', 'earnings'), ('MSFT', 'earnings_msft'), ('NVDA', 'earnings_nvda')]:
    raw[key] = qj.fmp.get_earnings_surprises(symbol=symbol)
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data25 / 29
Which names have rising EPS estimates but falling prices? AAPL, MSFT and NVDA. Revisions compare estimates for the same future fiscal period using EOD’s current and 30-days-ago fields. Price returns use the last session on or before each boundary. FMP supplies reported earnings dates. A snapshot screen is not a point-in-time backtest.
AAPL, MSFT and NVDA. Revisions compare estimates for the same future fiscal period using EOD’s current and 30-days-ago fields. Price returns use the last session on or before each boundary. FMP supplies reported earnings dates. A snapshot screen is not a point-in-time backtest.Saved calculation · API inputs captured 31 October 2026
26

Which reported positions would take longest to exit?

Combine a common institutional filer’s three-stock holdings sample with report-date trading volume and peer ownership.

Gateway SDK · provider methodsFMP + EOD
from quantjourney.sdk import QuantJourneyAPI
from sdk_crowded_exit import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'book_prices': qj.eod.get_historical_prices(
        symbols=['AAPL', 'MSFT', 'NVDA'], start_date='2025-01-01', end_date='2025-12-31', frequency='1d'),
}
for symbol, key in [('AAPL', 'holders'), ('MSFT', 'holders_msft'), ('NVDA', 'holders_nvda')]:
    raw[key] = qj.fmp.get_institutional_holders(symbol=symbol, year=2025, quarter=3, limit=100)
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data26 / 29
Which reported positions would take longest to exit? AAPL, MSFT and NVDA, holdings dated 2025-09-30. The selected CIK is the first common filer in the returned samples. Exit days assume 10% participation in trailing 20-session volume. Peer coverage is limited to returned records. This scenario does not predict forced selling or reconstruct the filer’s full portfolio.
AAPL, MSFT and NVDA, holdings dated 2025-09-30. The selected CIK is the first common filer in the returned samples. Exit days assume 10% participation in trailing 20-session volume. Peer coverage is limited to returned records. This scenario does not predict forced selling or reconstruct the filer’s full portfolio.Saved calculation · API inputs captured 31 October 2026
27

Is the portfolio becoming more exposed to one factor?

Estimate rolling monthly factor exposures for the notebook allocation and compare the market loading with VIX.

Gateway SDK · provider methodsEOD + Fama–French + Cboe VIX via FRED
from quantjourney.sdk import QuantJourneyAPI
from sdk_factor_regime import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'prices': qj.eod.get_historical_prices(
        symbols=['SPY', 'TLT', 'GLD', 'DBC', 'UUP'], start_date='2020-01-01', end_date='2025-12-31', frequency='1d'),
    'factors': qj.ff.get_factors(
        region='america', factor='5_factors'),
    'vix_history': qj.fred.get_fred_data_series_by_id(
        search_id='VIXCLS', start='2020-01-01', end='2025-12-31'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data27 / 29
Is the portfolio becoming more exposed to one factor? The notebook allocation uses fixed buy-and-hold starting weights. Each 24-month regression uses only its trailing observations. Five equity factors explain a projection of this multi-asset allocation, not every risk. Current-vintage factors and prices support descriptive analysis, not a point-in-time trading claim.
The notebook allocation uses fixed buy-and-hold starting weights. Each 24-month regression uses only its trailing observations. Five equity factors explain a projection of this multi-asset allocation, not every risk. Current-vintage factors and prices support descriptive analysis, not a point-in-time trading claim.Saved calculation · API inputs captured 31 October 2026
28

Do the sources support the same equity thesis?

Review earnings, cash conversion, insider filing activity and short interest without collapsing different dates into one score.

Gateway SDK · provider methodsFMP + SEC + FINRA
from quantjourney.sdk import QuantJourneyAPI
from sdk_equity_conviction import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'income': qj.fmp.get_income_statement(
        symbol='AAPL', period='annual', limit=5),
    'cash': qj.fmp.get_cash_flow_statement(
        symbol='AAPL', period='annual', limit=5),
    'sec_insider': qj.sec.get_insider_transactions(
        symbol='AAPL', limit=100),
    'short': qj.finra.get_short_interest(
        symbol='AAPL'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data28 / 29
Do the sources support the same equity thesis? AAPL. Financial statements, returned Form 4 filings and FINRA short interest retain different reporting dates. Form 4 counts measure filing activity, not net insider buying. This evidence dashboard is not a conviction score or recommendation.
AAPL. Financial statements, returned Form 4 filings and FINRA short interest retain different reporting dates. Form 4 counts measure filing activity, not net insider buying. This evidence dashboard is not a conviction score or recommendation.Saved calculation · API inputs captured 31 October 2026
29

How much of the post-earnings move was residual?

Fit market, sector and style exposures before an earnings event, then reconcile their contributions to the observed stock return.

Gateway SDK · provider methodsEOD + Fama–French + FMP
from quantjourney.sdk import QuantJourneyAPI
from sdk_event_attribution import analyze

qj = QuantJourneyAPI.from_env()
raw = {
    'prices': qj.eod.get_historical_prices(
        symbols=['AAPL', 'SPY', 'XLK'], start_date='2023-01-01', end_date='2025-12-31', frequency='1d'),
    'daily_factors': qj.ff.get_factors(
        region='america', factor='5_factors', frequency='daily', start_date='2023-01-01', end_date='2025-12-31'),
    'earnings': qj.fmp.get_earnings_surprises(
        symbol='AAPL'),
}
result, figure = analyze(raw)
print(result.head())
figure.show()

The preview imports the analysis helper from the full Python file below.

Download full notebook Download full Python code
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Original SDK workflow. Download the complete notebook and run it with your credentials.

Calculated from observed API data29 / 29
How much of the post-earnings move was residual? AAPL. A 120-session regression ends before the event date. The next five sessions use frozen coefficients. Contributions reconcile to the sum of daily arithmetic returns; they do not claim a compounded holding-period return or causal attribution. Sector is XLK minus SPY; style factors are SMB, HML, RMW and CMA.
AAPL. A 120-session regression ends before the event date. The next five sessions use frozen coefficients. Contributions reconcile to the sum of daily arithmetic returns; they do not claim a compounded holding-period return or causal attribution. Sector is XLK minus SPY; style factors are SMB, HML, RMW and CMA.Saved calculation · API inputs captured 31 October 2026

Additional registered routes.

Use domain routing for Polygon/Massive market data and Quiver disclosures. Select the provider for your request.

Polygon / MassiveMarket aggregates
from quantjourney.sdk import QuantJourneyAPI

qj = QuantJourneyAPI.from_env()
bars = qj.domains.call(
    "equity.pricing.get_aggregates",
    provider="polygon",
    ticker="SPY", timespan="day",
    from_date="2024-01-01", to_date="2025-12-31",
    limit=5000,
)
QuiverCongressional disclosures
from quantjourney.sdk import QuantJourneyAPI

qj = QuantJourneyAPI.from_env()
disclosures = qj.domains.call(
    "government.political.get_congress_trade_history",
    provider="quiver", ticker="AAPL",
)

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